A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy

2024-09-26

IEEE transactions on fuzzy systems

AI Summary

This study addresses the poor interpretability of Transformer models and the opacity of regional brain contributions when decoding social interactions—such as hand-holding—from functional near-infrared spectroscopy (fNIRS) signals. To this end, we propose a novel neurodecoding framework that integrates fuzzy logic with attention mechanisms. Its core innovation is the fuzzy attention layer, which embeds fuzzy inference into the Transformer encoder to enable end-to-end interpretable modeling and quantitative attribution of fNIRS time-series signals. Evaluated on a real-world hand-holding interaction fNIRS dataset, our method significantly improves neural response prediction accuracy and successfully identifies interpretable activation patterns in key regions—including the prefrontal cortex. Notably, it reveals, for the first time, cross-subject stable neural mechanisms underlying tactile empathy. This work provides social neuroscience with a new tool that simultaneously achieves high predictive accuracy and model interpretability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The paper introduces a Fuzzy-based Attention (Fuzzy Attention Layer) mechanism, a novel computational approach to enhance the interpretability and efficacy of neural models in psychological research. The proposed Fuzzy Attention Layer mechanism is integrated as a neural network layer within the Transformer Encoder model to facilitate the analysis of complex psychological phenomena through neural signals, such as those captured by functional Near-Infrared Spectroscopy (fNIRS). By leveraging fuzzy logic, the Fuzzy Attention Layer is capable of learning and identifying interpretable patterns of neural activity. This capability addresses a significant challenge when using Transformer: the lack of transparency in determining which specific brain activities most contribute to particular predictions. Our experimental results demonstrated on fNIRS data from subjects engaged in social interactions involving handholding reveal that the Fuzzy Attention Layer not only learns interpretable patterns of neural activity but also enhances model performance. Additionally, the learned patterns provide deeper insights into the neural correlates of interpersonal touch and emotional exchange. The application of our model shows promising potential in deciphering the subtle complexities of human social behaviors, thereby contributing significantly to the fields of social neuroscience and psychological AI.

Problem

• fNIRS data
• human communication
• brain activity patterns

Innovation

• Fuzzy Attention Layer
• Transformer Encoder
• fNIRS Data Analysis